mlsanityData to 7 Oct 2026

Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks

arXiv 1909.09586 ↗First posted 12 Sep 2019cs.NE cs.CL cs.LGcited by 103

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Abstract (arXiv)

Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most powerful dynamic classifiers publicly known. The network itself and the related learning algorithms are reasonably well documented to get an idea how it works. This paper will shed more light into understanding how LSTM-RNNs evolved and why they work impressively well, focusing on the early, ground-breaking publications. We significantly improved documentation and fixed a number of errors and inconsistencies that accumulated in previous publications. To support understanding we as well revised and unified the notation used.

Key facts. Cited by 103 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2019.

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Citing papers per year: 2021: 7; 2022: 14; 2023: 18; 2024: 29; 2025: 25; 2026: 10.

103 papers.

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38 more
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  13. 2025Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Managementcited by 4
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50 most cited of 103 All 103 (JSON)